IDEAS home Printed from https://ideas.repec.org/p/ces/ceswps/_1283.html
   My bibliography  Save this paper

Nonparametric Regression and the Detection of Turning Points in the Ifo Business Climate

Author

Listed:
  • Klaus Abberger

Abstract

Business climate indicators are used to receive early signals for turning points in the general business cycle. Therefore methods for the detection of turning points in time series are required. Estimations of slopes of a smooth component in the data can be calculated with local polynomial regression. A change in the sign of the slope can be interpreted as a turning point. A plug-in method is used for data-based bandwidth choice. Since in practice the identification of turning points at the actual boundary of the time series is of special interest, this situation is discussed in more detail. The nonparametric approach is applied to the Ifo Business Climate to demonstrate the application of the nonparametric approach and to analyze the time lead of the indicator.

Suggested Citation

  • Klaus Abberger, 2004. "Nonparametric Regression and the Detection of Turning Points in the Ifo Business Climate," CESifo Working Paper Series 1283, CESifo.
    • Handle: RePEc:ces:ceswps:_1283
    as

    Download full text from publisher

    File URL: https://www.cesifo.org/DocDL/cesifo1_wp1283.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lars-Erik Öller & Lasse Koskinen, 2004. "A classifying procedure for signalling turning points," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(3), pages 197-214.
    2. Eva Andersson & David Bock & Marianne Frisén, 2004. "Detection of Turning Points in Business Cycles," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2004(1), pages 93-108.
    3. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    4. Christian Hott & André Kunkel & Gernot Nerb, 2007. "The Accuracy of Turning Point Predictions with the Ifo Business Climate," Chapters, in: Georg Goldrian (ed.), Handbook of Survey-Based Business Cycle Analysis, chapter 14, Edward Elgar Publishing.
    5. Jan Beran & Yuanhua Feng, 2002. "Local Polynomial Fitting with Long-Memory, Short-Memory and Antipersistent Errors," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 54(2), pages 291-311, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sesma Julio & Husted Bryan & Banks Jerry, 2014. "La medición del desempeño social empresarial a través de las redes sociales," Contaduría y Administración, Accounting and Management, vol. 59(2), pages 121-143, abril-jun.
    2. Franco Mariuzzo & Patrick Paul Walsh & Ciara Whelan, 2004. "EU Merger Control in Differentiated Product Industries," CESifo Working Paper Series 1312, CESifo.
    3. Robert Lehmann, 2023. "The Forecasting Power of the ifo Business Survey," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 19(1), pages 43-94, March.
    4. Кокорева Мария Сергеевна & Степанова Анастасия Николаевна, 2012. "Financial architecture and corporate performance: evidence from Russia," Journal of Corporate Finance Research Корпоративные финансы, CyberLeninka;Федеральное государственное автономное образовательное учреждение высшего образования «Национальный исследовательский университет «Высшая школа экономики», issue 2 (22), pages 34-44.
    5. Abberger, Klaus, 2007. "Qualitative business surveys and the assessment of employment -- A case study for Germany," International Journal of Forecasting, Elsevier, vol. 23(2), pages 249-258.
    6. Sascha O. Becker & Klaus Wohlrabe, 2008. "European Data Watch: Micro Data at the Ifo Institute for Economic Research – The “Ifo Business Survey”, Usage and Access," Schmollers Jahrbuch : Journal of Applied Social Science Studies / Zeitschrift für Wirtschafts- und Sozialwissenschaften, Duncker & Humblot, Berlin, vol. 128(2), pages 307-319.
    7. Klaus Abberger & Sascha Becker & Barbara Hofmann & Klaus Wohlrabe, 2007. "Mikrodaten im ifo Institut für Wirtschaftsforschung – Bestand, Verwendung und Zugang," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 1(1), pages 27-42, June.
    8. Stefan Sauer & Klaus Wohlrabe, 2020. "ifo Handbuch der Konjunkturumfragen," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 88.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Andersson, Eva, 2007. "Effect of dependency in systems for multivariate surveillance," Research Reports 2007:1, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    2. Avanzi, Benjamin & Taylor, Greg & Vu, Phuong Anh & Wong, Bernard, 2020. "A multivariate evolutionary generalised linear model framework with adaptive estimation for claims reserving," Insurance: Mathematics and Economics, Elsevier, vol. 93(C), pages 50-71.
    3. François R. Velde, 2009. "Chronicle of a Deflation Unforetold," Journal of Political Economy, University of Chicago Press, vol. 117(4), pages 591-634, August.
    4. Wen Xu, 2016. "Estimation of Dynamic Panel Data Models with Stochastic Volatility Using Particle Filters," Econometrics, MDPI, vol. 4(4), pages 1-13, October.
    5. Alejandro Rodriguez & Esther Ruiz, 2009. "Bootstrap prediction intervals in state–space models," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(2), pages 167-178, March.
    6. Parrini, Alessandro, 2013. "Importance Sampling for Portfolio Credit Risk in Factor Copula Models," MPRA Paper 103745, University Library of Munich, Germany.
    7. Salman Huseynov, 2021. "Long and short memory in dynamic term structure models," CREATES Research Papers 2021-15, Department of Economics and Business Economics, Aarhus University.
    8. Tsionas, Mike G., 2021. "Bayesian forecasting with the structural damped trend model," International Journal of Production Economics, Elsevier, vol. 234(C).
    9. Tommaso Proietti, 2002. "Some Reflections on Trend-Cycle Decompositions with Correlated Components," Econometrics 0209002, University Library of Munich, Germany.
    10. Tobias Hartl & Roland Jucknewitz, 2022. "Approximate state space modelling of unobserved fractional components," Econometric Reviews, Taylor & Francis Journals, vol. 41(1), pages 75-98, January.
    11. Broto Carmen & Ruiz Esther, 2009. "Testing for Conditional Heteroscedasticity in the Components of Inflation," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 13(2), pages 1-30, May.
    12. Marczak, Martyna & Proietti, Tommaso, 2016. "Outlier detection in structural time series models: The indicator saturation approach," International Journal of Forecasting, Elsevier, vol. 32(1), pages 180-202.
    13. Oreste Napolitano & Alberto Montagnoli, 2010. "The European Unemployment Gap and the Role of Monetary Policy," Economics Bulletin, AccessEcon, vol. 30(2), pages 1346-1358.
    14. Joshua Chan & Arnaud Doucet & Roberto León-González & Rodney W. Strachan, 2018. "Multivariate Stochastic Volatility with Co-Heteroscedasticity," Working Paper series 18-38, Rimini Centre for Economic Analysis.
    15. Siem Jan Koopman & Joao Valle e Azevedo, 2003. "Measuring Synchronisation and Convergence of Business Cycles," Tinbergen Institute Discussion Papers 03-052/4, Tinbergen Institute.
    16. Aruoba, S. BoraÄŸan & Diebold, Francis X. & Scotti, Chiara, 2009. "Real-Time Measurement of Business Conditions," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 417-427.
    17. Omar H. M. N. Bashar, 2015. "The Trickle‐down Effect of the Mining Boom in Australia: Fact or Myth?," The Economic Record, The Economic Society of Australia, vol. 91(S1), pages 94-108, June.
    18. Funke, Michael & Tsang, Andrew, 2019. "The direction and intensity of China's monetary policy conduct: A dynamic factor modelling approach," BOFIT Discussion Papers 8/2019, Bank of Finland Institute for Emerging Economies (BOFIT).
    19. Pelagatti, Matteo & Maranzano, Paolo, 2021. "Assessing the effectiveness of the Italian risk-zones policy during the second wave of COVID-19," Health Policy, Elsevier, vol. 125(9), pages 1188-1199.
    20. David de Antonio Liedo, 2014. "Nowcasting Belgium," Working Paper Research 256, National Bank of Belgium.

    More about this item

    Keywords

    Nonparametric regression; slope estimation; turning points; business climate indicators;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ces:ceswps:_1283. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Klaus Wohlrabe (email available below). General contact details of provider: https://edirc.repec.org/data/cesifde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.